"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
AI EngineeringData Engineering for AI
Build the Data Foundation AI Needs to Thrive
Design and implement scalable data pipelines, lakehouses, and feature stores that feed your AI models clean, consistent, and timely data — because AI is only as good as the data powering it.
Data Capabilities
AI-Ready Data Engineering
From data ingestion through feature serving, we build the data infrastructure that makes AI models reliable and accurate in production.
Data Lakehouse Architecture
Design modern lakehouses on Databricks, Snowflake, or BigQuery with Delta Lake / Iceberg for ACID transactions and time-travel capabilities.
Real-Time Streaming Pipelines
Apache Kafka, Apache Flink, and Spark Streaming pipelines processing millions of events per second with sub-second latency for AI feature computation.
ETL/ELT Data Integration
Connect 100+ data sources — CRMs, ERPs, databases, SaaS APIs — with reliable, monitored pipelines that keep your data warehouse current.
Feature Store Engineering
Build centralized feature stores ensuring training-serving consistency, feature reuse, and point-in-time correctness for production ML models.
Data Quality & Observability
Automated data quality checks, schema validation, anomaly detection, and lineage tracking so data issues are caught before they corrupt models.
Data Governance & Security
Row-level security, column masking, data cataloging, GDPR/CCPA compliance frameworks, and access audit logging for enterprise data platforms.
Why Choose Us
Why Agile Infoways for Data Engineering
We've built data platforms processing petabytes of data for AI systems across financial services, retail, and healthcare.
AI-First Data Design
We design data infrastructure specifically for ML workloads — not just analytics. Feature stores, training pipelines, and serving layers are first-class citizens.
Data Quality Obsession
90% of AI failures trace to data problems. We instrument every pipeline with quality gates that catch drift, nulls, and schema changes before they reach models.
dbt & Modern Data Stack
Deep expertise in dbt, Airbyte, Fivetran, and the modern data stack — bringing software engineering practices to data transformation.
Multi-Cloud Expertise
Certified architects across AWS, Azure, and GCP data platforms — Redshift, Synapse, BigQuery, Databricks, Snowflake, and beyond.
Our Capability
Data Engineering Stack
Best-in-class tools for building production-grade AI data infrastructure.
Databricks / Snowflake
Unified analytics and AI platforms with Delta Lake and automatic scaling for petabyte workloads.
Apache Kafka / Flink
Event streaming backbone for real-time data pipelines with exactly-once processing guarantees.
dbt (data build tool)
SQL-first data transformation with testing, documentation, and lineage for data warehouse layers.
Feast / Tecton
Production feature stores with offline/online serving, time-travel, and feature versioning.
Great Expectations / Monte Carlo
Data quality validation and observability with automated anomaly detection and alerting.
Airbyte / Fivetran
300+ pre-built connectors for reliable ELT with incremental syncing and change data capture.
Our ApproachHow We Build
How We Build
Data Platforms
A systematic approach from data audit through production platform with data quality at every layer.
01
Data Audit & Architecture Design
02
Pipeline Development & Integration
03
Feature Store & AI Readiness
04
Observability, Governance & Scale
Step 01
Data Audit & Architecture Design
Inventory all data sources, assess quality and latency requirements, identify AI use cases, and design the target data architecture and governance model.
Source inventoryQuality assessmentAI use-case mappingTarget architecture
Step 02
Pipeline Development & Integration
Build ingestion pipelines from all sources, implement transformation logic in dbt, set up orchestration with Airflow or Prefect, and deploy quality checks.
Ingestion pipelinesdbt transformationsOrchestration setupQuality gates
Step 03
Feature Store & AI Readiness
Implement feature engineering pipelines, deploy feature store with online/offline serving, validate point-in-time correctness, and connect to ML training.
Feature pipelinesOnline/offline servingTraining data validationML integration
Step 04
Observability, Governance & Scale
Deploy data observability tools, implement catalog and governance policies, optimize pipeline performance, and document platform for team self-service.
Data observabilityCatalog & lineagePerformance optimizationSelf-service docs
Our ApproachHow We Build
How We Build
Data Platforms
A systematic approach from data audit through production platform with data quality at every layer.
Step 01
Data Audit & Architecture Design
Inventory all data sources, assess quality and latency requirements, identify AI use cases, and design the target data architecture and governance model.
Source inventoryQuality assessmentAI use-case mappingTarget architecture
Step 02
Pipeline Development & Integration
Build ingestion pipelines from all sources, implement transformation logic in dbt, set up orchestration with Airflow or Prefect, and deploy quality checks.
Ingestion pipelinesdbt transformationsOrchestration setupQuality gates
Step 03
Feature Store & AI Readiness
Implement feature engineering pipelines, deploy feature store with online/offline serving, validate point-in-time correctness, and connect to ML training.
Feature pipelinesOnline/offline servingTraining data validationML integration
Step 04
Observability, Governance & Scale
Deploy data observability tools, implement catalog and governance policies, optimize pipeline performance, and document platform for team self-service.
Data observabilityCatalog & lineagePerformance optimizationSelf-service docs
Use Cases
Data Engineering in Production
Real data platforms powering AI systems at enterprise scale.
RE
RetailUnified Commerce Data Platform
The Challenge
Retailer with 15 data silos — POS, e-commerce, loyalty, supply chain — unable to build accurate demand forecasting models.
The Outcome
Unified lakehouse on Databricks ingesting all 15 sources, powering demand models that reduced stockouts by 35% and overstock by 28%.
DatabricksDelta LakeKafkadbt
FI
FintechReal-Time Risk Feature Platform
The Challenge
Risk models using batch features 24 hours stale — missing fraud patterns that emerged intraday.
The Outcome
Flink streaming platform computing risk features in real time, reducing fraud detection latency from 24 hours to 200ms.
Apache FlinkFeast feature storeKafkaExactly-once
HE
HealthcareHIPAA Data Lakehouse
The Challenge
Health system with patient data spread across 8 EHR systems, preventing any cross-system AI analysis.
The Outcome
HIPAA-compliant lakehouse unifying all EHR sources with PHI masking, enabling population health AI models for first time.
SnowflakeHL7 FHIRPHI maskingData governance
MA
ManufacturingIoT Sensor Data Pipeline
The Challenge
Factory with 50,000 IoT sensors generating 2TB/day with no reliable pipeline — predictive maintenance models starved of data.
The Outcome
Kafka + Spark Streaming pipeline ingesting all sensors in real time, cutting equipment downtime by 42% through predictive maintenance.
Kafka StreamsSpark Structured StreamingTime-series DBAnomaly detection
Industries
AI Solutions Across Key Verticals
Deep domain expertise meets cutting-edge AI — delivering results where they matter most.
Client Stories
Built With Trust. Proven in Production.
Hear directly from the leaders who partnered with us to ship AI-powered products, modernize platforms, and move faster than they thought possible.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
Let's Build Something Remarkable Together
Book a call or drop us a message. Our team will respond within 24 hours.
Schedule a Discovery Call
30-minute consultation · Free
UTC
Loading available slots…
Times shown in UTC